As mentioned earlier, neural network practitioners may feel that the neural network content of the book is a bit light, and this part can be almost entirely skipped by these readers.

for people coming from more traditional branches of statistical learning, Chapter 5 is still well worth reading.


The relative unclarity of how to determine the arguments of discourse relations in an RST tree complicates efforts to capture semantically relevant information in these structures, and thus undermines a semantic argument for analyzing discourse in terms of constituent structures like RST tree structures.

although most computational approaches have eschewed a principled answer to this question, almost all proposals agree that the EDUs should play a part in the relational structure, as should the relations that link them.


Without a notion of head, two dependency trees will represent one RST tree consisting of one multinuclear relation.

assuming some convention to determine the head in an RST tree will result in a dependency structure that represents two RST c-trees; for instance, assuming that a tree consisting of one multinuclear relation has its head in the leftmost argument will mean that the RST trees [a N , b] and [a N , b N ] yield the same unlabeled dependency structure, a → b. Consequently, RST non-terminal constituents have been translated as dependencies with some information loss; see for example .


To go back to our case in Example (2), we see that the candidate tree has three errors on what the head of a span is, which correspond to the three UAS arrow errors.

if the heads of spans are all correct, then the spans will determine correct attachments.


On the other hand, if the heads of spans are all correct, then the spans will determine correct attachments.

to furnish an equivalent dependency based measure to our δ SH , we need also to consider the order in which dependents are attached to the head.


Our model outperforms previous work significantly.

to identity anaphors, which indicate coreference between a noun phrase and its antecedent, bridging anaphors link to their antecedent(s) via lexico-semantic, frame, or encyclopedic relations.


Barzilay and Lapata (2008) model local coherence with the entity grid based on coreference only.

example (1) does not exhibit any coreferential entity coherence, and therefore entity coherence can only be established when bridging is resolved.


They achieved a kappa score of 0.78 for six top-level categories.

the confusion matrix in Riester, Lorenz, and Seemann (2010) shows that the anaphoric bridging category is frequently confused with other categories: The two annotators agreed on fewer than a third of bridging anaphors.


(2004a) combines lexico-semantic and salience features to resolve mereological bridging in the GNOME corpus.

their results came from a limited evaluation setting: In the first two experiments they distinguished only between the correct antecedent and one or three false candidates.


There is partial overlap between bridging resolution and implicit semantic role labeling (i.e., in some bridging cases, antecedents are implicit semantic roles of bridging anaphors).

bridging resolution considers all possible nominal bridging anaphors in running text.


In Example (1), the antecedent the Polish center could trigger the anaphor walls.

bridging anaphors can be solely indicated by referential patterns as nonsense Example 16shows that the wug is clearly a bridging anaphor although we do not know the antecedent.


For instance, walls in Example (1) are necessary parts of the antecedent the Polish center according to common sense knowledge.

windows and carpets are only probable or inducible parts of a building but still function as bridging anaphors in Example (1).


f 9 IsSetElement is used to identify set-membership bridging cases (see Example (12)), by checking whether the mention head is a number or indefinite pronoun (one, some, none, many, most) or modified by each, one.

not all numbers are bridging cases, and we use f 11 IsYear to exclude some such cases.


For our general model formulation, we assume the antecedent entity setting as the general case which subsumes the other setting.

candidate generation, feature computation, and evaluation will vary for the two settings and therefore we explore both scenarios in the experiments.


Set-membership relations between anaphor and antecedent evade the preposition pattern, because the anaphor often has no common noun head (Example (29)).

in such a bridging relation, the antecedent is semantically compatible with the verb the anaphor depends on.


This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets.

with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize.


Because this task may be very complex (in fact the search space may grow exponentially), a technique called synchronous parsing is used to constrain the search space.

this approach exhibits the problem of spurious ambiguity, which that paper elaborates in depth.


For instance, context-free grammars, which correspond to push-down automata, have access to unlimited amounts of memory.

they are known not to be efficiently learnable.


Trying to improve on the results with k = 3, we expected that adding a (limited size) right context, which is performed using k, l-local merging, would provide a gentle way of restricting state-merging (with respect to k-testable with the same k values).


In the case of the 3-testable merging criterion, state q 3 is merged with other states (like q 3 ) that show the same incoming path of three transitions (a b c).

in the 2, 1-local merging criterion case, state q 2 is merged with other states (like q 2 ) that also have the same incoming path of two transitions (a b) and an outgoing transition (c).


The HDP model for the root nodes (on the right) illustrates the generation of words yelling, talking, repairs.

to the DPs in the child nodes, in the HDP model, the stems/suffixes are shared across all root HDPs.


Although we assume a stem+suffix segmentation, other types of segmentation, such as prefix+stem, are also covered.

stem alterations and infixation are not covered in our model.


We say yes to the constant flow of peer-review journal publications and their impact; (...) All of us are in this game, too.


As a result, other formalisms that were considerably more expressive than TAG have laid claim to the Joshian mantle of Mild Context Sensitivity, showing that it has become a highly influential meme in the field.

it has since been shown that the artificial permutation-complete language MIX3, consisting of all permutations over the strings of the TAL a n b n c n , is a Multiple Context Free Language (MCFL).


I think the surveyed papers support this use of BLEU, since most of the system-level BLEU-human correlations for MT reported in the survey are Medium or High (Figure 5).

the evidence does not support using BLEU to evaluate other types of NLP systems (outside of MT), and it does not support using BLEU to evaluate individual texts rather than NLP systems.


Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art.

a systematic examination of ensemble methods for NLI has yet to be conducted.


Random forests provide the biggest boost, improving performance by almost 10%.

this is still lower than our LDA meta-classifier.


29 29 Ionescu, Popescu, and Cahill (2016) do carry out experiments on raw documents from the Norwegian ASK corpus, and reach accuracies up to 68%.

their set-up is rather different, following Pepper (2012).


For instance, in applications such as parsing or translation, we work with a fixed grammar, so it might seem that the universal recognition problem is of little practical relevance.

it is worth remembering that for these applications, we are primarily interested in the structural descriptions that the grammar assigns to a generated sentence, not in the membership of the sentence per se.


A derivation for this sentence is shown in the upper half of Figure 3.

the schema cannot be universally active in English, as this would cause the grammar to also accept strings such as *Kahn blocked skillfully a powerful by Rivaldo shot, which is witnessed by the derivation in the lower half of Figure 3 (a dagger † marks the problematic step).


In part 1 of the reduction (Section 3.1.2), this allows us to "guess" arbitrary truth assignments for the variables in the clause φ.

the possibility to write grammars with lexical ambiguity is an essential feature of all interesting formalisms for natural language syntax, including also TAG.


When the machine is in an existential state, it accepts the input if there is at least one transition that eventually leads to an accepting state.

when the machine is in a universal state, it accepts input only if every possible transition eventually leads to an accepting state.


These elementary pieces are specially designed to satisfy two useful properties: First, each elementary piece can be stored using an amount of space that does not depend on the length of w. Second, elementary pieces can be shared among different derivations of w under G. The algorithm then uses dynamic programming to construct and store in a multidimensional parsing table all possible elementary pieces pertaining to the derivations of w under G. From such a table one can directly check whether w ∈ L(G).

despite the fact that the number of derivations of w under G can grow exponentially with the length of w, the two properties of elementary pieces allow the algorithm to run in time polynomial in the length of w. the runtime is not bounded by a polynomial function in the size of G, as should be expected from the hardness results reported in Section 3.1.


The computational effect of grammar structure and grammar size on the parsing problem is rather well understood for several formalisms currently used in computational linguistics, including context-free grammar and TAG.

to the best of our knowledge, this problem has not been investigated before for VW-CCG or other versions of CCG; see, for instance, Kuhlmann and Satta (2014) for discussion.


From the computational perspective, ε-entries represent the boundary between the results in Section 3 and Section 4.

because we do not know whether the classes NP and EXPTIME can be separated, we cannot draw any precise conclusion about the role of ε-entries in the parsing problem.


Schabes (1990) reports that the universal recognition problem for TAG can be decided in time O(|G| 2 |w| 6 ), where |G| is the size of the input grammar G and |w| is the length of the input sentence w. One could hope then to efficiently solve the universal recognition problem for VW-CCG by translating an input VW-CCG G into an equivalent TAG G , and then applying to G and the input string any standard recognition method for the latter class.

the part of the equivalence proof by Vijay-Shanker and Weir (1994) showing how to translate VW-CCG to TAG requires the instantiation of a number of elementary trees in G that is exponential in |G|.


Because we can also translate any TAG into an equivalent VW-CCG without blowing up the size of the grammar, following the construction by Vijay-Shanker and Weir (1994), we conclude that VW-CCG is more succinct than TAG.

the price we have to pay for this gain in expressivity is the extra parsing complexity of VW-CCG.


We want to emphasize that our method works in an unsupervised fashion and is not restricted to certain POS classes.

most of the competitive methods require POS filtering as a pre-processing step in order to do their statistics.


Because of comparison reasons, the first evaluation that uses POS filtering (see Section 3.6) is restricted to noun compounds.

the remaining experiments in Sections 3.7 and 3.8 are not restricted to any particular POS.


Only for the P@100, can the word2vec-based method beat the t-test and frequency baselines.

for all other measures, the performance is similar to these baselines or even inferior, and significantly worse than using DRUID with JoBimText.


Figure 2, using solely the DRUID method or the combined variation with the logfrequency lead to the best ranking for the first 1,000 ranked candidates.

both methods are outperformed beyond the first 1,000 ranked candidates by the MFinformed DRUID variations.


and the t-test with stopword filtering, the DRUID method yields the best scores for 6 out of the 32 languages.

if we multiply the logarithmic frequency by the DRUID measure, we gain the best performance for 30 languages.


Extracting the most similar terms that are nested in w results in the first split candidate set, called similar candidate units.

only for few terms do we observe nested candidates in the most similar words.


We have examined schemes of priority ordering for integrating information from different candidate sets-for example, using the similar candidate units first and only applying the other candidate sets if no split was found.

preliminary experiments revealed that it was always beneficial to generate splits based on all three candidate sets and use the geometric mean scoring as outlined above to select the best split as decomposition of a word.


As observed in Table 17, the highest precision using the JoBimText similarities is achieved with the similar candidate units.

the recall is lowest because for many words no information is available.


Interestingly, we observe an opposite trend for word2vec.

the best overall performance is achieved with the generated dictionary, which yields an F1 measure of 0.9583 using JoBimText and 0.9627 using word2vec.


We tried to resolve this issue by recursively splitting words with nested compounds also contained in the data set.


Comparing two methods for the generation of distributional semantic models within SECOS, we obtain the best results for German, Dutch, and Afrikaans using word2vec.

for Finnish the best results are achieved with JoBimText.


Furthermore, the feature-based hash sampling included only contextual features (in the form of n-gram co-occurrence information), and did not consider orthographic features.

our log-linear model integrates both type-level orthographic features and token-level bigram frequencies.


To address this challenge, we apply IMH sampling, which relies on a proposal distribution and does not require normalization.

finding an appropriate proposal distribution can sometimes be challenging, as it needs to be close to the true distribution for faster mixing and must be easy to sample from.


For the forced expectation, one possibility is to use the bigram language model p(e 1 e 2 ) as a proposal distribution.

the bigram language model did not work well in practice.


r Accept the new sample with the probability: The IMH sampling reduces the complexity of the forced expectation estimation to O(N F N), 1 which is significantly less than the complexity of O(N F VN) in the case of Gibbs sampling.

we could not apply IMH while estimating the full expectation, as finding a suitable proposal distribution is more complicated.


At a high level, given an observed French sentence, it samples a hidden English sequence according to p(e|f) in order to estimate the forced expectation term of the update, and then samples a French sentence according to p(f|e) to estimate the full expectation, as shown in Algorithm 1.

because the individual English words are not independent, due to the bigram language model, the sampling of p(e|f) is itself broken down into a sequence of Gibbs sampling steps, sampling one word at a time while holding the others fixed, as shown in Algorithm 2.


For example, the Spanish word "madre" means "mother" in English, but our model gave the highest score to the English word "made" due to the high orthographic similarity.

such error cases are rare compared with the improvement.


The accuracies reported here are significantly lower than those achieved by modern supervised methods (and unsupervised methods with large corpora).

our results required no more than 1,000 lines of data from each language, and preserved accuracy with as little as 100 lines of data.


One can argue that the antecedents in such cases (i.e., this chapter and this section) are big chunks of text and therefore non-nominal.

though these are certainly interesting cases, we do not focus on them in this article.


Instead, it can only refer to the snake here.

in Example (28b), reference to the situation by that is possible, and due to that prior mention, subsequent reference by it is also possible.


(NYT) Here, the distance between the anaphor and the antecedent is small: The antecedent of this fact occurs in the preceding clause.

in Example (31), the antecedent of this question occurs four sentences away from the anaphor sentence.


For instance, replacing either (d) or (e) in Example (34) with the (constructed) sentence in (e ) will violate the right-frontier constraint, because that in (e ) accesses the closed-off information about House A.

it seems to be a fairly natural continuation of the conversation, especially if it were uttered in the course of a spontaneous conversation that had not been prepared in detail in advance.


Thus the referring function states that that refers to the event of Engine 1 getting to Avon, which takes two hours.

to Eckert and Strube (2000), who present a system design that was not implemented, Byron (2002Byron ( , 2004) presents an implemented system.


They use surface-based features and information that is readily available and easy to gather automatically.

knowledge-poor methods do not tend to be particularly effective, as is evidenced by relatively low recall in general.


Because unification can pass information across unbounded structures, this can be thought of as reducing Merge to Move.

generalized Phrase Structure grammar (gazdar 1981), Tree Adjoining grammar (TAg; Joshi and Levy 1982), and Combinatory Categorial grammar (CCg, Ades and Steedman, 1982) sought to reduce Move to various forms of local merger.


The class of languages characterizable by these formalisms fell within the requirements of what Joshi (1988) called "mild context sensitivity" (MCS), which proposed as a criterion for what could count as a computationally "reasonable" theory of natural languages-informally speaking, that they are polynomially recognizable, and exhibit constant growth and some limit on crossing dependencies.

the MCS class is much much larger than the CCG/TAG languages, including the multiple context free languages and even (under certain further assumptions) the languages of Chomskian minimalism, so it seems appropriate to distinguish TAG and CCG as "slightly noncontext-free" (SNCF, with apologies to the French railroad company).


Also, we do not see the switchboard as a competitor to WebLicht or the LAPPS Grid.

each new predefined workflow in WebLicht can be advertised by and directly invoked from the switchboard, hence directing more user traffic to WebLicht.


If the author did not send the data and/or source code (nor replied that it was not possible to send the requested information), we sent a second and final e-mail on 24 October 2017.

to the first e-mail, this second e-mail was sent to all authors of the paper, and the deadline for sending the information was extended to 19 November 2017.


Grice makes the important assumptions that participants in a discourse are rational agents and that they are governed by cooperative principles.

in some cases involving non-literal readings or negotiation, agents do not always have rational communicative behavior.


might be followed by "I love it too," which has a similar structure and proposition as the first utterance: (1) I I love love that it game too the second response might be "I hate that game," in which the contrast between love and hate shows the disalignment.


For example, if you are talking about food, and you say how yummy the french fries are, then the stance focus is the fries and they are evaluated positively.


Moreover, this ALIGNMENT must have a previous utterance with which to align or disalign.

aFFECT is a comment-internal relationship.


2015b), public opinions and user behavior understanding on societal issues (Pak and Paroubek 2010;Popescu and Pennacchiotti 2010;Kouloumpis, Wilson, and Moore 2011), and so forth.

the explosive growth of microblog data far outpaces human beings' speed of reading and understanding.


In addition to "topic" modeling, it has also inspired discourse (Crook, Granell, and Pulman 2009;Ritter, Cherry, and Dolan 2010;Joty, Carenini, and Lin 2011) detection without supervision or with weak supervision.

none of the aforementioned work jointly infers discourse and topics on microblog conversations, which is a gap the present article fills.


2015) combines short text aggregation and topic induction into a unified model.

in SATM, no prior knowledge is given to ensure the quality of text aggregation, which will further affect the performance of topic inference.


For example, to the best of our knowledge, there currently exists no high-quality word embedding corpus for Chinese social media.

to these prior methods, our model does not have the prerequisite to an external resource, whose general applicability in cold-start scenarios is therefore ensured.


2000;Cohen, Carvalho, and Mitchell 2004;Bangalore, Fabbrizio, and Stent 2006).

dA definition is generally domain-specific and usually involves the manual designs from experts.


Most of these approaches have considered utterances in isolation.

even humans have difficulty sometimes in recognizing sarcastic intent when considering an utterance in isolation (Wallace et al.


For the neural networks models, similar to the results on the IAC v2 data set, the LSTM models that read both the context and the current turn outperform the LSTM model that reads only the current turn (LSTM ct ).

unlike the IAC v2 corpus, for Twitter, we observe that for the LSTM without attention, the single LSTM architecture (i.e., LSTM ct+pt ) performs better, that is, 72% F1 between the sarcastic and non-sarcastic category (average), which is around 4 percentage points better than the multiple LSTMs (i.e., LSTM ct +LSTM pt ).


(2014) showed that by providing additional conversation context, humans could identify sarcastic utterances that they were unable to identify without the context.

it will be useful to understand whether a specific part of the conversation context triggers the sarcastic reply.


Also, the attention model selects (i.e., second highest weight) sentence S2 from the P TURN ("he died because of it"), which also shows that the model captures opposite sentiment between the conversation context and the sarcastic post.

from Figure 8, we notice that some of the Turkers choose the third sentence S3 ("sure russia fuels the conflict, but he didnt have to go there") in addition to sentence S1 from the context P TURN.


For this task, the agreement between the attention weights of the models and humans (using majority voting) is lower than for the previous task.

the IAA between Turkers is also just moderate (α between 0.66 and 0.72), which shows that this is inherently a difficult task.


We show that even if the training data in Reddit is ten times larger, it did not make much impact in our experiments.

the Reddit corpus consists of several subreddits, so it might be interesting in the future to experiment with training data from a particular genre of subreddit (e.g., political forums).


The typology provides valuable insights into the linguistic realization of irony that could improve its automatic detection (e.g., the correlation between irony markers and irony activation types).

given the complexity of identifying such pragmatic devices as demonstrated by the inter-annotator agreement study, it is not clear to which extent it would be computationally feasible to detect the irony categories they propose.


As shown in Table 11, keeping only content words discards pronouns, determiners, and so forth, and makes the targets more likely to yield many tweets.

it also discards elements that are crucial for the semantics of a target, such as numbers and figures.


We can  conclude that applying sentiment analysis to the tweets is a viable method to define the prototypical sentiment related to the situations (yielding an accuracy of up to 72.20%).

our targets being very specific and restricted by the limited search space when using the Twitter Search API, we were only able to collect tweets for fewer than half of the targets.


As can be inferred from Figure 7, collecting more tweets seems to have a moderate effect on the overall sentiment analysis performance (91% vs. 94%).

the increase seems to stagnate at 750 tweets and the scores even decline as the number of tweets further increases.


First, it should be noted that a contrast feature (based on explicit polarity words) was already included as part of the sentiment lexicon features (see Section 3).

the feature is not included in the final irony classifier, as the experiments revealed that combining lexical, semantic, and syntactic features without sentiment features works best.


Furthermore, while the annotation guidelines distinguish between different irony types, the present research approaches irony detection as a binary classification task and hence provides insights into the feasibility of irony detection in general.

fine-grained irony classification might be worthwhile in the future, to be able to detect specifically ironic tweets in which a polarity inversion takes place.


Networks with ReLU have a lower run time and tend to show better convergence (Talathi and Vartak 2015).

reLU has the disadvantage of dying cells (the dying reLU problem), but this can be overcome by using a variant called Leaky reLU.


The incorrect decisions are underlined and marked with red color.

unlike synchronous conversations (e.g., meeting, phone), modeling conversational dependencies between sentences in an asynchronous conversation is challenging, especially when the thread structure (e.g., "reply-to" links between comments) is missing, which is also our case.


2006;Ravi and Kim 2007;Bhatia, Biyani, and Mitra 2014) tackle the task at the comment level, and use task-specific tagsets.

in this work we are interested in identifying speech acts at the sentence level, and also using a standard tagset like the ones defined in SWBD or MRDA.


In this case, because the out-of-domain labeled data set (MRDA) is much larger, it overwhelms the model, inducing features that are not relevant for the task in the target domain.

when we provide the model with some labeled in-domain examples  Confusion matrices for (a) MLP conv-glove and (b) B-LSTM conv-glove on the test sets of QC3, TA, and BC3.


One interesting future work would be to learn the underlying conversational structure automatically.

we believe that in order to learn an effective model, this would require more labeled data.


11 These values could also be due to the collection of fewer answers per compound for some of the data sets.

there is no clear tendency in the variation of the standard deviation of the answers and the number of participants n. The values of σ are quite homogeneous, ranging from 1.05 for EN-comp 90 (head) to 1.27 for EN-comp Ext (head).


Table 4 reports the best configurations for the EN-comp, FR-comp, and PT-comp data sets.

to determine whether the Spearman scores obtained are robust and generalizable, in this section we report evaluation using cross-validation.


In EN-comp, all differences between the two highest Spearman values for each DSM were significant, according to Wilcoxon's signrank test, except for PPMI-thresh, whereas in FR-comp and PT-comp they were significant only for PPMI-TopK and lexvec.

note that the top two results are often both obtained on representations based on lemmas.


As in Figure 9, Figure 10 shows a random distribution of improvements.

the outliers have the opposite pattern, indicating that large reclassifications due to pc geom tend to favor idiomatic instead of compositional compounds.


We also calculated the correlation between PMI and the human-prediction difference (diff), to determine if DSMs build less precise vectors for less conventionalized compounds (approximated as those with lower PMI).

no statistically significant correlation was found for most DSMs (Table 10, column ρ[diff, PMI]).


For example, given the declarative sentence NVIDIA was founded by Jen-Hsun Huang and Chris Malachowsky, the distant supervision approach creates the utterance NVIDIA was founded by Jen-Hsun Huang and blank and the corresponding denotation Chris Malachowsky.

the actual question users may ask is Who founded NVIDIA together with Jen-Hsun Huang.


Different from the surface syntax, GR graphs are not constrained to trees.

the tree structure is a fundamental consideration of the design for the majority of existing parsing algorithms.


On the one hand, each subgraph is simple enough to allow efficient construction.

the combination of all subgraphs enables the whole target GR structure to be produced.


However, in the particular neural parsing architecture employed here, beam search performs significantly worse than the dynamic oracle strategy.

do note that beam search and structured learning may be very helpful for neural parsing models of other architectures (Weiss et al.


Second, it reflects a concrete technique by which regular sound Greek z u g o n th u g a (ter-) -n u -(os) e r u th (rós) Regular sound correspondences across four Indo-European languages, illustrated with help of alignments along the lines of Anttila (1972, page 246).

to the original illustration, lost sounds are displayed with help of the dash "-" as a gap symbol, while missing words (where no reflex in Gothic or Latin could be found) are represented by the "∅" symbol.


If we had reflexes for all languages under investigation in all cognate sets, the compatibility would not be a problem, since we could simply group all identical sites with each other, and the task could be considered as solved.

since it is rather the exception than the norm to have all reflexes for all cognate sets in all languages, we will always find possible alternative groupings for the alignment sites.


What may come as a surprise is that the reduction of the data by 25% and 50% does not seem to influence the accuracy of prediction in all data sets.

in the Chinese and the Polynesian data sets, we find even slightly higher accuracy scores for the larger data reduction.


This is because semantics of longer contexts is more difficult to capture than that of shorter contexts.

the performance of all models improved when the length of contexts increases from (5, 10] to (10, ).


The current method requires a huge amount of training data (i.e., context-response pairs) to learn a matching model.

it is too expensive to obtain large-scale (e.g., millions of) human labeled pairs in practice.


Fourth, a P&P parameter typically controls a very small set of features.

if our linguistic parameter is turned on, it more or less modifies all the feature generation probabilities.


In contrast, if our linguistic parameter is turned on, it more or less modifies all the feature generation probabilities.

we can expect a small number of linguistic parameters to dominate the probabilities because weights are drawn from a heavy-tailed distribution, as we describe in Section 4.1.


We decided not to follow the TimeML classification because aspectual types and syntax do not have a primary importance for historians in the interpretation of texts.

the types and subtypes defined in ACE, ERE, and Event Nugget are too limited and do not allow us to identify events comprehensively.


Moreover, adjectives are underrepresented in WordNet supersenses with only three possible classes: adj.ppl, adj.all, and adj.pert.

adjectives have an important role in our annotation in the context of copular constructions.


The historical nature of the texts and the diversity of topics covered by the news make them particularly interesting for annotation.

travel narratives are not much explored in computational linguistics.


The best F1 (29.27%) is given by a context of [±1].

as already noted with the experiments on features, precision and recall are not balanced, showing a difference of 11.73 points.


In the experiments reported in Reimers and Gurevych (2017a), discarding character embeddings is never the best option.

in our case the highest precision, recall, and F1 are achieved without using them.


As for GloVe, there is not much difference between the two dimensions (300 and 100).

the overall results are almost 3 points lower for event detection and 4 points lower for classification compared to the model employing the Komninos and Manandhar embeddings.


Compared to the results of inter-annotator agreement, we notice that three of the classes with higher F1 had also a perfect agreement between human annotators: This means that COMMUNICATION, PHYSICAL SENSATIONS, and LIFE-HEALTH are the less ambiguous classes to be identified.

other classes with perfect IAA have very low scores or even an F1 equal to zero: this is the case for FOOD-FARMING, EDUCATION, and ECONOMY.


As a result in the English-to-Japanese translation task, the proposed model obtained better RIBES and BLEU scores than the sequence-to-sequence NMT model both with and without reversed input with the same number of parameters.

the same significant improvement is observed only over the sequence-to-sequence NMT model with reversed inputs in the Chinese-to-Japanese translation task.


Inflecting number names incorrectly in, say, Russian does lead to a lack of naturalness, but the result would be usable in most cases.

producing number names that are not value-preserving renders the result worse than unusable, since the user would be grossly misinformed.


To a first approximation, one can handle all of these cases using hand-written grammars-as in Sproat (1997) and Ebden and Sproat (2014).

different aspects of the problem are better treated using different approaches.


Note that the amount of training dataabout 10 million tokens-is perhaps an order of magnitude less data than is commonly used for translation between high-resource languages.

our task is significantly easier than real translation.


In the previous sections, we have assumed that the input sequence is pre-segmented into tokens.

in real-world applications the normalization model needs to be fed with segmented output produced by a segmenter.


The grammars used in a largely hand-crafted text normalization system tend to be quite complex because they need to produce the contextually appropriate rendition of a particular token.

covering grammars-henceforth CGs-are lightweight grammars, which enumerate the reasonable possible verbalizations for an input, rather than trying to specify the correct version for a specific instance.


Accordingly, the problem of graph parsing has been conceived in such a way that a graph structure and a graph grammar are provided as input, and the goal is to produce as output one or more grammar derivations that generate the input graph itself.

this problem setting does not apply to the scenario of semantic parsing for natural language.


Because of this similarity, algorithms for string-to-tree parsing that use dynamic programming can be naturally extended to string-to-graph parsing.

when we do so, we soon realize that there is a gap in computational complexity between the two cases, because of the structural differences between non-crossing trees and general graphs.


String-to-graph parsing is very closely related to the so-called problem of dependency semantic parsing, where one is given an ordered sequence of vertices and has to output a maximum weight graph defined over those vertices, representing a most-likely semantic analysis of the associated sentence.

with string-to-graph parsing, dependency semantic parsing does not require any hardwired input grammar generating a set of valid graphs.


For instance, if we parse the sentence into spans [0, 4] and [4, 5], we have I(0, 4) = {1, 3, 4} and I(4, 5) = {5}, which adds up to a bag of 4 vertices at the node with span [0,5].

we also observe that while the span [0, 4] has three connections, to outside vertices, all of the connections are made to the same vertex 5.


In the outside decomposition, the outside connections of span [0, 4] would not be repeatedly counted and the span would have a contribution of 1 to the total width, resulting in a decomposition with smaller width.

an inside tree decomposition can also produce a treewidth smaller than an outside tree decomposition.


We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages.

just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations.


In this work we follow up on the findings of Rabinovich, Ordan, and Wintner (2017), who, by using language representations consisting of manually specified feature vectors, find that the structure of a language representation space is approximately preserved by translation.

their analysis only stretches as far as finding a correlation between their language representations and genetic distance, even though the latter is correlated to several other factors.


Given that this new vector is constituted by verbal contexts, it belongs to the same vector space as verbs, and therefore it can be combined with the word vector of catch.

c in Equation (2) represents the vector set of those nouns occurring as direct object of catch in the corpus: {train, bus, disease, .


The accuracy of this strategy (0.335) is higher than that obtained by the Dict-First baseline, even though there is just a slight improvement with a p-value = 0.03.

the Dict-Corpus-Based strategy is outperformed by the non-compositional baselines in a significant way (p-value = 0.006).


Like in our experiments in Section 6, it uses a distributional thesaurus as input, as well as multiple pre-and post-processing stages to filter the input graph and disambiguate individual nodes.

to Pachenko et al., here we directly apply the WATSET algorithm to obtain the resulting distributional semantic classes instead of using a sophisticated parametric pipeline that performs a sequence of clustering and pruning steps.


RuWordNet is more domain-specific in terms of vocabulary, so our input set of generic synonymy dictionaries has a limited coverage on this data set.

recall calculated on YARN is substantially higher as this resource was manually built on the basis of synonymy dictionaries used in our experiments.


An example of the sense graph built by WATSET for two senses of the lexical unit "java" using CW log for local clustering.

to Figure 16, in this disambiguated distributional thesaurus the node corresponding to the lexical unit "java" is split: java 11 is connected to programming languages and java 17 is connected to drinks.


because the average size of a cluster in this experiment is much higher (Table 18 and Figure 14), we found that the enumeration of 2-combinations of semantic class elements is not computationally tractable in reasonable time on relatively large data sets like the ones we use in this experiment.


Among the exponents, that of the long-range correlation, ξ, differs largely among the four data sets considered thus far.

the other exponents generally take similar values for the data sets.


It is therefore not a challenge for language models to satisfy Zipf's and Heaps' laws.

the metrics of long memory were capable of quantifying the quality of machine-generated texts.


The Taylor exponents of the n-gram language models were consistently ζ = 0.50, which indicates the absence of long memory.

the neural language models had Taylor exponents of ζ > 0.50, which indicates the presence of long memory in the generated texts (right).


The Taylor exponent was ζ = 0.50, and the other metrics also indicated that the generated text did not have long-range dependence.

the RNNs with a gating mechanism (LSTM, GRU, and QRNNs) could reproduce long memory behavior.


In terms of BLEU, the best-performing GAN model varied among RankGAN with BLEU-2, LeakGAN with BLEU-3 and BLEU4, and TextGAN with BLEU-5.

textGAN was the best model in terms of eval-AWD-LStM.


no previous work has evaluated the performance of automatic evaluation metrics on the output of neural versus other MT approaches.


The plots for the different metrics look fairly similar, with the exception of BLEU.

to the rest of the metrics, BLEU assigns very low scores to the majority of translations due to the sparseness of n-gram matches with the references.


In such a setting, quality levels can be defined in a straightforward way based on the discrete manual evaluation scores.

the techniques discussed in the previous section cannot be applied, as a certain amount of variation inside each level would be required in order to measure the correlation between metric and human scores.


The annotations in this data set were collected using Amazon Mechanical Turk, which often raises questions about the reliability of the data.

as described in Graham, Mathur, and Baldwin (2015), a rigorous quality control was conducted in order to filter out Average quality score and standard deviation of the scores assigned to the same sentences by different annotators.


As mentioned in Section 2, some work has been done comparing MT evaluation metrics for statistical systems versus rule-based systems.

the behavior of metrics when it comes to neural MT (NMT), the new paradigm in MT research and development, has not yet been inspected.


In order to isolate the impact of the type of human judgments on the meta-evaluation results, in the following analysis we use the data sets that belong to the news domain (with the exception of the WMT17 Quality Estimation data set), have English as the target language, and have the outputs of MT systems based on statistical approach (with the exception of the WMT16 data sets that contain different types of MT systems).

other factors, such as the average level of MT quality or the reliability of manual evaluation scores, are more difficult to control.


Automatic MT evaluation remains a prominent field of research, with various evaluation methods proposed every year.

studies describing the weaknesses and strengths of the existing approaches are very rare.


Typological documentation is limited by the fact that the evidence available for each language is highly unbalanced and many languages are not even recorded in a written form.

3 large typological databases such as WALS (Dryer and Haspelmath 2013) nevertheless have an impressive coverage (syntactic features for up to 1,519 languages).


Still, the language vectors belonging to the same cluster display some microvariations in individual features.

5(a) shows clusters differing from language genealogy: for instance, English and Czech are merged, although they belong to different genera (Germanic and Slavic).


With the exception of Naseem, Barzilay, and Globerson (2012), who treated typological information as a latent variable, automatically acquired typological features have not been integrated into algorithms for NLP applications to date.

they have several advantages over manually crafted features.


As shown in § 4.2, most of the typology-savvy algorithms thus far exploited features extracted from manually crafted databases.

this approach is riddled with several shortcomings, which are reflected in the small performance improvements observed in § 5.4.


As a consequence, these approaches typically select a limited subset of features from a single data set (WALS) and focus on a single aspect of variation (typically word order).

typological databases also cover other important features, related to predicate-argument structure (ValPaL), phonology (LAPSyD, PHOIBLE, StressTyp2), and lexical semantics (IDS, ASJP), which are currently largely neglected by the multilingual NLP community.


As a consequence, it does not fit well with the gradient and contextual models of machine learning.

typological databases are originally created from raw linguistic data.


In this experiment, we know beforehand that there is a relationship between words, and our aim is to identify the type of relationship.

in many situations this kind of a priori information is not available.


These rules are used to augment the set of target terms iteratively from a seed collection.

these approaches highly rely on hand-coded rules and are inflexible.


These works indicate the importance of invariant syntactic information between words as a bridge between different domains.

their drawback is reflected by restraining the pivot information within a few manually defined rules that are fixed and inflexible.


The update process involves a gradient reversal layer for the domain predictor that results in the following updates: The DAN as defined here assumes similar data distributions between the source and target domains in general.

in many cases, the input data do not commit a universal but multimodal distributions.


For example, which weather parameters (temperature, wind chill) are influential versus what body parameters (heart rate, body temperature) are anomalous is heavily dependent on the domain at hand, such as weather or healthcare, respectively.

the surface realization part of language generation may not be as domain-dependent and can, thus, be designed in a reusable and scalable way.


The system is primarily designed for taking a structured table with variable schema as input and producing a coherent paragraph description pertaining to the facts in the table.

it can also work with other structured data formats such as graphs and key-value pairs (in the form of JSONs) as input.


We observe that the endto-end WEBNLGMODEL does better than WIKIBIOMODEL.

our proposed system clearly gives the best performance, demonstrating the capability of generalizing in unseen domains and structured data in a more complex form such as a multi-row and multi-column table.


As we can see, all systems are significantly involved in producing the correct output in the test data.

the TRIPLE2TEXT system is selected a fewer number of times than the other two systems.


2017), and set-to-sequence generation (Vinyals, Bengio, and Kudlur 2016) can also act as building blocks for generation from structured data.

none of these works consider the morphologic and linguistic variations of output words as we consider for simple language generation.


In some cases it is sufficient to know merely the range of argumentative types used in order to grade student essays (Ong, Litman, and Brusilovsky 2014), to know what stance an essay takes toward a proposition in order to check that it provides appropriate evidence to back-up its stance (Persing and Ng 2015), or whether a claim is verifiable in order to flag these in online discussions (Park and Cardie 2014).

if the goal is to reconstruct enthymemes (Razuvayevskaya and Teufel 2017) (see also the discussion of Feng and Hirst [2011] in Section 8.2) or ask critical questions about support relations, we also need to extract the nature of the argumentation schemes being used.


Although formally structured dialogues can be captured and exploited in this way, many real world dialogues follow only very limited rules and the challenge of identifying the argumentative structure in free form discussion is complex.

even very informal dialogues nevertheless provide additional data beyond that available in monologue, which can be used to help constrain the task.


The model integrates the two-level formalism and a unification-based formalism.

to other works, we propose to separate the treatment of sequential and non-sequential morphotactic constraints.


General dictionaries testify all possible senses of a given word; typical word collocates acquired from dictionaries tend to cover the entire range of possible senses of a headword.

unrestricted texts re ect actual usage and possibly bear witness to senses which are relevant to a speci c domain only.


We w ere able to extract all SI's relative to the entire K B .

we report here an intrinsic evaluation of the accuracy of acquired centroids which i n volves only a small subset of our results, since provision of a reference class typology is extremely labour intensive.


In multilingual application development within Microsoft research, grammar sharing has extensively been exploited ± [Pin96], [GLPR97].

all these approaches are rather opportunistic in the sense that existing grammatical descriptions based on existing grammar models were explored.


Fergus could easily be augmented with a preprocessor that maps a semantic representation to our syntactic input; this is not the focus of our research.

there are two more important di erences.


1994;Tugwell 1995), but these approaches have remained context-free in their generative power.

lexical-Functional Grammar (Kaplan & Bresnan 1982) is known to be beyond context-free.


We will assess their model on the LFG-annotated Verbmobil and Homecentre corpora in section 3 of this paper.

we will also assess an alternative definition of fragment probability which is a refinement of Bod & Kaplan's model.


In this paper we will use Bod's subtree-to-rule conversion method for studying the behavior of probabilistic against non-probabilistic DOP for different maximum subtree sizes.

we will not use Bod's Monte Carlo sampling technique from complete derivation forests, as this turns out to be computationally impractical for our larger corpora.


an SCFG) which annotates the nonterminals with headwords.

such a headlexicalized stochastic grammar does not capture dependencies between nonheadwords (such as more and than in the WSJ construction carry more people than cargo where neither more nor than are headwords of the NP-constituent more people than cargo) , whereas a frontier-lexicalized DOP model using large subtrees does capture these dependencies since it includes subtrees in which e.g.


In essence, we have a robust text analysis system for identification of proper names and technical terms, since these are most likely to carry the bulk of the semantic load in a document.

in addition to simple identification of certain phrasal types, capabilities also exist for identifying their variants (contractions, abbreviations, colloquial uses, etc.)


We focus on some strategies for incorporating segmentation results in the summary generation process.

unlike (Kan et al., 1998) (whose work also seeks to leverage linear segmentation for the explicit purposes of document summarization), we further take the view that with an appropriate interface metaphor-where the user has an overview of the relationships between a summary sentence, the key salient phrases within it, and its enclosing discourse segment-a sequence of visually demarkated segments can impart a lot of information directly leading to in-depth perception of the summary, as it relates to the full document (Boguraev and Neff, 2000).


In a sense, the apparatus of restrictions allows to represent UWs as disambiguated English words.

restrictions allow to denote concepts which are absent in English.


come(met>ship) is interpreted as 'come and the method of coming is a ship'.

here is an example of a UNL expression for the sentence (2) language differences are a barrier to the smooth flow of information in our society.


In the lexicon we used, nouns are, on average, members of 2 semantic classes.

the semantic classes are ordered so that the most typical use comes first.


1 See Annex for examples and the definition of binding constraints.

to this, however, the formal and computational handling of binding constraints has presented non-negligible resistance when it comes to their integration into the representation and processing of grammatical knowledge.


anaphoric definite descriptions, ruled by Principle C) may be assigned the respective equation.

these difficulties turn out to be solved, the LFG variant of the coindexation methodology presents the same type of problems of Johnson's approach.


This is detectable by instrumentation, as discussed in Sec.4.1.

once there is a testsuite, it has to be used economically, avoiding redundant tests.


We do not need criteria for tree decomposition and category specialization, and we can use the standard parsing algorithm.

the e ciency gains are not as big as those reported by Rayner and Carter, 1996 but note that we cannot measure parsing times alone, so we need to compare to their speed-up factor of 10.


95), an intelligent dictionary lookup which achieves some word sense disambiguation using word context (part-of speech and multiword expressions (MWEs) 6 recognition).

locolex choices remain purely syntactic.


Our methods are close to those used for positioning unknown words in thesauri.

the two issues can be di erentiated with respect to the manipulated data.


Our experiments show that exogeneous categorization is noticeably the most e cient of both approaches.

it requires much more knowledge sources and computational overhead.


For example, the name of a country is usually not mentioned in a news article reporting an event that happened in that country.

the country name is important in foreign news.


The voting strategy from reporters gives a shorter summarization in terms of user-preferred languages.

it also misses some unique information reported only by one site.


The syntactic and semantic categories of unknown words in principle can be determined by their content and contextual information.

many difficult problems have to be solved.


The step 4 thus achieves the resolution of both semantic ambiguities of the head and the modifier.

only the category of the head is our target of resolution.


Neighborhoods are quite successful in guessing the second half of such lists.

there were a few big losers, e.g., articles that summarize the major stories of the day, week and year.


For example, for the text in Figure 1, whose rhetorical structure is shown in Figure 2, the head of span [5,7] is unit 5 because the head of the immediate nucleus, the elementary unit 5, is 5.

the head of span [6,7] is the list h6,7i because both immediate children are nuclei of a multinuclear relation.


The antecedent Genetic Therapy, Inc. appears in unit 1; therefore, using VT we search back 2 units (units 8 and 1) to find a correct antecedent.

to resolve the same reference using a linear model, four units (units 8, 7, 6, and 5) must be examined before Genetic Therapy is found.


In this case, we consider that the effort is equal to k. As a consequence, for small ks the effort required to establish co-referential links is similar for both theories, because both can establish only a limited number of links.

as k increases, the effort computed over the entire corpus diverges dramatically: using the Discourse-VT model, the search space for co-referential links is reduced by about 800 units for a corpus containing roughly 1200 referential expressions.


If one treats all discourse units in the preceding discourse equally, the increase is statistically significant only when a discourse-based coreference system looks back at most four discourse units in order to establish co-referential links.

if one assumes that proximity plays an important role in establishing coreferential links and that referential expressions are more likely to be linked to referees that were used recently in discourse, the increase is statistically significant no matter how many units a discourse-based co-reference system looks back in order to establish co-referential links.


A similar ambiguity is exhibited by (25): (i) there was no such (single) person that would give P. a book and M. flowers, (ii) P. did not get a book and M. did not get flowers.

there is no such ambiguity in (26 'To Peter, nobody gave a book and to Mary, flowers.'


Whereas (Gundel et al., 1993) do not attempt to make their focus notion operationalizable, this has been attempted by further developments of centering.

these have mostly been applied to the pronoun resolution problem.


The left-corner grammar transform converts a left-recursive grammar into a non-left-recursive one: a top-down parser using a left-corner transformed grammar simulates a left-corner parser using the original grammar Rosenkrantz and Lewis II, 1970;Aho and Ullman, 1972.

the left-corner transformed grammar can be signi cantly larger than the original grammar, causing numerous problems.


First, note that LC P G, the result of applying the standard left-corner grammar transform to G, has approximately 20 times the number of productions that G has.

lC td;lc l0 G, the result of applying the selective left-corner grammar transformation with factorization, has approximately 1.4 times the number of productions that G has.


For example, in this paper we assumed that one would always choose a left-corner production set that includes the minimal set L 0 required to ensure that the transformed grammar is not left-recursive.

roark and Johnson 1999 report good performance from a stochastically-guided top-down parser, suggesting that left-recursion is not always fatal.


more recursive semantic phenomena such as possessives and other complex noun phrases are added to the grammar the resulting machines become larger.

the computational consequences of this can be lessened by lazy evaluation techniques (Mohri, 1997) and we believe that this finitestate approach to constructing semantic representations is viable for a broad range of sophisticated language interface tasks.


Some mixed-script entries could be handled as syntactic compounds, for example, ID [ai dii kaado="ID card"] could be derived from ID NOUN + NOUN .

many such items are preferably treated as lexical entries because In addition, many Japanese verbs and adjectives (and words derived from them) have a variety of accepted spellings associated with okurigana, optional characters representing inflectional endings.


And ∆ n,h , the distance between the two words, is widely used, because this attribute is believed to strongly affect whether those two words are going to be related.

in the statistical model proposed in this paper, P (n → h) depends not only on the attributes of the tree M, but also on alternative trees in the parse forest generated by the grammar.


They report that this kind of contextual information improves accuracy.

the model has to assume the independency of all the random variables, which may cause some errors.


Some research institutes have constructed Japanese case frame dictionaries manually (Ikehara et al., 1997;Information-Technology Promotion Agency, Japan, 1987).

it is quite expensive, or almost impossible to construct a wide-coverage case frame dictionary by hand.


A noun modified by a clause is usually a case component for the verb of the modifying clause.

there is no case-marker for their relation.


For example, the case frame of the verb naru 'become' differs depending on its ni (dative) case as follows: with become a friend In most cases, the main case components are placed just in front of the light verbs so that the automatic parser can detect their relations  As shown in Table 1, KNP detects heads of case components in fairly high accuracy.

in order to collect much reliable data, we discarded modifier-head relations in the automatically parsed corpora in the following cases: • When CMs of case components disappear because of topic markers or others.


Based on the conditions above, case components of each verb are collected from the parsed corpora, and the collected data are considered as case frames of verbs.

if the frequency of a CM is very low compared to other CMs, it might be collected because of parse errors.


We can thus expect to have identical SPL expressions for Bulgarian, Czech and Russian in many cases, although these may be realized by diverging syntactic structures.

we also allow for the case in which there is no commonality at this level and even the SPL expressions diverge.


Whether this is true remains an open problem.

the previous intuition seems to hold anyway when the two analogies to be concatenated do not have any symbol in common.


For instance, ay : az = by : x is not acceptable when x = zb.

the three other possible analogies meet intuition, so that the following hypothesis may be laid.


In the tagging phase, instead of using (4)-(6), the input can be constructed simply as ipt t0i = g t0i 1 OPT(0i); 10where i = 1 ; 1 11 ; l , and OPT(0i) means the output of the tagger for the ith word before the target word.

in the training process, the output of the tagger is not always correct and cannot be fed back to the inputs directly.


This process is repeated until N (w t ) 6 = U nknown or (l;r) = ( 0 ; 0).

to make the same set of connection weights of the neuro tagger with the largest (l;r) a v ailable as much a s possible when using short inputs for tagging, in training phase the neuro tagger is regarded as a neural network that has gradually grown from small one.


In the last decade, members of the computational linguistics community have adopted a perspective on discourse based primarily on either Rhetorical Structure Theory or Grosz and Sidner's Theory.

only recently, researchers have started to investigate the relationship between the two perspectives.


Hence, axiom (8) explicates the relationship between the structure of discourse and intentional dominance.

axiom (9) explicates the relationship between intentional dominance and discourse structure.


One basic rule for MWTU representation is that an MWTU is composed of only lexical morphemes if possible, that is, grammatical morphemes such as particles and the endings of a word will be extracted in the representation because of the above characteristics which are freely inserted and omitted.

grammatical morphemes affecting the meanings of MWTUs must be described.


This factor was preserved under 5 and 500 repetitions of the same parse.

speed was not the main issue in developing this setup, but rather simplicity and ease of implementation.


In such cases, we must add new features to the analysis to create a situation in which many category-exclusive rules can be applied.

it is not sucient to use categoryexclusive rules.


When multiple rules have the same probability and similarity, the method takes the examples used by the rules having the highest probability and the highest similarity, and chooses the category with the larger number of examples as the desired answer, in the same way as in Method 1.

when category-exclusive rules having more than one frequency exist, the above procedure is performed after eliminating all of the categoryexclusive rules having one frequency.


They are similar to MID-3D in that they use planning mechanisms in content planning.

in presentation systems, unlike dialogue systems, the user just watches the presentation without changing her his view.


As a result, two Main-Acts looking at the user and requesting to try to do the action and two Subsidiary-Acts showing how to do the action, then resetting the state are set as subgoals and returned to the DM.

if the object is not visible to the user, hOperator 2i is selected.


This task, which we will call text structuring, i s t ypically addressed through a micro-planning phase that determines the content of successive sentences.

documents of realistic complexity require richer TSs including, for example, vertical lists, sub-sections, and clauses separated by semi-colons.


The converse does not hold: for instance, an RS of the form R 1 (R 2 (p 1 p 2 ) p 3 ) can be realized by a paragraph of three sentences, one for each proposition, even though this TS contains no node dominating the propositions (p 1 and p 2 ) that are grouped by R 2 .

when this happens, the propositions grouped together in the RS must remain consecutive in the TS solutions in which p 3 comes inbetween p 1 and p 2 are prohibited.


10,000 sentence pairs to those which included a source-noun from this 2 Similar results for pseudo-disambiguation were obtained for a simpler approach which avoids another EM application for probabilistic class labeling.

heren andĉ was chosen such that fĉv;n = max c;n fLCv;n+1pLCcjv;n: the sensitivity to class-parameters was lost in this approach.


The most direct point of comparison is the method of Dagan and Itai 1994 which gives 91.4 precision 92.7 standardized and 62.1 e ectiveness 66.8 standardized on 103 test examples for target word selection in the transfer of Hebrew to English.

compensating this high precision measure for the low e ectiveness gives values comparable to our results.


The Senseval standard is clearly beaten by the earlier results of Yarowsky 1995 96.5 precision and Sch tze 1992 92 precision.

a comparison to these refrom his random baseline 28.5 by taking 100=28:5; reversely, Dagan and Itai's 1994 random baseline can be calculated as 100=2:27 = 44:05.


Firstly, these approaches were evaluated on words with two clearly distant senses which w ere determined by the experimenters.

our method was evalutated on randomly selected actual translations of a large bilingual corpus.


We found that both of these variants of our grammar produced reasonable recognition, though the Word+ grammar was very inaccurate.

a three-words-per-category grammar could not produce successful speech recognition.


From these experiments, we w ere unable to isolate any simple set of factors to explain which grammars would be problematic for speech recognition.

the numbers of transitions per graph in a PFSG did seem suggestive of a factor.


in, on, behind, : : : ) that are required by the verb.

adjuncts can use a wider variety of prepositions.


we can use verbs that appear in relative clauses.

there are two main drawbacks: Treebanks are expensive to build and so the techniques presented here have to work with less data.


One way to begin a negotiation subdialogue is to express doubt at a proposition.

expressions of doubt occur in a variety of forms, each of which conveys information about the nature of the doubt that is important for the subsequent resolution of the con ict.


In our research, we draw on their notion of identifying how features of the generation context correlate with how an utterance should be expressed.

our work di ers from theirs in that we m ust deal with an agent's beliefs motivating his doubt and we consider a wider range of variations in realization.


To identify how expressions of doubt are realized in naturally occurring dialogue and how these realizations convey the requisite beliefs, we analyzed features of individual expressions of doubt extracted from natural corpora, and correlated the various forms of the utterances with the features of the underlying beliefs.

as explained in Section 3.3, the use of machine learning techniques was not appropriate due to the nature of our corpus.


The technique of backward analysis of Japanese sentences has been used in rule-based methods, for example (Fujita, 1988).

there are several difficulties with rulebased methods.


Many such methods used heuristics to make deterministic decisions (and backtracking if it fails in a searching) rather than using a scoring scheme.

the combination of the backward analysis and the statistical method has very strong advantages, one of which is the beam search.


In principle, the wider the beam search width, the more analyses can be retained and the better the accuracy can be expected.

the result is somewhat different from the expectation.


We have to rely on necessarily imperfect heuristics.

we can specialize the general French deconverter to produce specialized servers for different tasks and different (target) sublanguages.


The UNL language defines the interface structure to be used by applications (either a hypergraph or a colored graph).

it does not restrict the choice of the data to be encoded.


The idea to use UNL for directly creating documents gets here an indirect and perhaps paradoxical support, although it is clear that considerable progress and innovative interface design will be needed to make it practical.

the UNL language proves flexible enough to be used by very different projects.


they must nevertheless denote individuals familiar to conversants if they are successfully to refer.

there is another class of referring expressions in relation to which we believe the concept of uniqueness of meaning does have a n essential role to play.


We also start our search for the referents of pronouns and other centred entities in the current discourse state, which is necessary if we are to resolve such referring expressions as her" in Mary took John with her."

referring expressions containing the property centred are prevented from being dereferenced to salient entities, thus ensuring that the constraint of disjoint reference is met.


First, we consider a case in which there is a discourse antecedent of sorts: In this way, we prove that the referring expression makes sense, i.e., denotes.

unlike in the previous cases, we do not dereference to a familiar referent.


It also has interesting theoretical implications, since it suggests a way in which pragmatic theo-ries of reference resolution, like F amiliarity Theory, and semantic theories, like Russell's, may be reconciled.

it is fair to say that the success of the approach is not yet proven.


Although "to make a map" and "exective" are not wrong translations, they are irrelevant i n the computer manual context.

the domain dictionary reduces confusion caused by the wrong word selection.


This algorithm reports about 90% accuracy of Thai open compound extraction.

the algorithm emphasizes on open compound extraction and has to limit the range of n-gram to 4-20 grams for the computational reason.


Given the identity of thematic role mapped to subject and object positions, we expect to observe the same noun occurring at times as subject of the verb, and at other times as object of the verb.

for object-drop verbs, the thematic role of the subject of the intransitive is identical to that of the subject of the transitive, not the object of the transitive.


While unergatives are already accurately classi ed without trans, inspection of the change in class labels reveals that the addition of trans to the set improves performance on unaccusatives by helping to distinguish them from object-drops.

in this case, we also observe a loss in precision of unergatives, since some object-drops are now classi ed as unergatives.


On the one hand, the algorithm does not perform at expert level, as indicated by the fact that, for all experts, the lowest agreement score is with the program.

the accuracy achieved by the program of 69.5 is only 1.5 less than one of the human experts in comparison to the gold standard.


The experimental results show that our method is powerful, and suited to the classi cation of lexical items.

we h a ve not yet addressed the problem of verbs that can have m ultiple classi cations.


There is very little semantic dependency in the grammar rules, which is essential if the grammar is to be domain-independent.

the grammar rules are elaborately conditioned on morphological and syntactic features, enabling much finer-grained parsing analyses than just relying on a small number of basic parts-of-speech (POS).


Table 2: Results of applying the proposed method to direct translations of the metonymies in (Kamei and Wakao, 1992  Semantic Relation The method proposed in this paper identifies implicit terms for the explicit term in a metonymy.

it is not concerned with the semantic relation between an explicit term and implicit term, because such semantic relations are not directly expressed in corpora, i.e.


Previous work on anaphora resolution has yielded a rich basis of theories and heuristics for finding antecedents.

most research to date has neglected an important potential cue that is only available in spoken data: prosody.


Byron and Stent (1998) adapted this approach, which had previously been applied to text, for spoken dialogs, but with limited success.

to personal pronouns, demonstratives do not rely on calculations of salience.


They tested the effect of accented sentence-initial demonstratives that co-specify with the preceding sentence on the resolution of ambiguous personal pronouns, and found that the pronoun antecedents switched when the demonstrative was accented (Fretheim et al., 1997).

to our knowledge, there are no studies that compare the co-specification preferences of accented vs. unaccented demonstratives.


The expected (average) value of the instance results will stay the same.

the chances of getting an unusual result can change.


Summarization of written documents has recently been a focus for much research in NLP e.g., Mani and Maybury, 1997;AAAI, 1998;Mani et al., 1998;ACL, 2000, to name some of the major events in this eld in the past few years.

very little attention has been given so far to the summarization of spoken language, e v en less of conversations vs. monological texts.


No linguistic analyses are taken into account in these approaches.

in further research the authors plan to integrate linguistic knowledge such as in ectional analysis of verbs, nouns and adjectives.


An MT system with high coverage and not-toobad" quality can be useful in a Web-application where a great variety of texts are to be translated for occasional users which w ant to grasp the basic ideas of a foreign text.

a system with high quality and restricted coverage might b e useful for in-house MT-applications or a controlled language.


then Pc ij = Pc ij jw i ; t i :  If there is a case relation or a modi cation relation in two constituents, coverage heuristics designate it is easier to add the smaller tree to the larger one than to merge the two medium sized trees.

in the coordination relation, it is easier to merge two medium sized trees.


Techniques, for example, for proper name recognition and classification are well known.

good quality name recognition software is only freely available at the present for English.


Solving this problem might not only be useful in the event of detecting such signals from space, but also, by deliberately ignoring preconceptions based on human texts, may provide us with some better understanding of what language really is.

we need to start somewhere, and our initial investigations -which this paper summarises -make some basic assumptions (which we would hope to relax in later research).


The research community benefits from access to published corpora not available for commercial use.

a corporation that needs data for development and testing purposes is much more restricted.


The researchers used tag set size of only 3, including function, content and punctuation in the rule.

korean is a post-positional agglutinative language.


For example, all proper nouns "NE" and nouns "NN" can be retrieved by pos= "NE" | "NN" As usual, structural identity can be expressed by the use of logical variables.

variables must not occur in the scope of negation, since this would introduce the computational overhead of inequality constraints.


For example, Ringger (1995) reports an average error rate of 30% for recognizing careful, spontaneous speech on a specific topic.

the error rate of paced speech can be as low as 5% if the vocabulary is severely limited or if the text is highly predictable and the system is tuned to that particular genre.


Disadvantages to this approach are that it relies on timesensitive texts, texts obtained by this approach are constrained to referencing specific events, and nontrivial work by humans is still necessary.

our goal is to extract bilingual text pairs automatically from any kind of bilingual comparable corpora.


The best hand created pattern based system seems to have a wide coverage dictionary for person, organization and location names and achieved very good accuracy for those categories.

the hand created pattern based system failed to capture the evaluation specific patterns like "the middle of April".


Table 2 show that increasing the size of the training corpus enhances the accuracy incrementally.

the point of diminishing return is reached when the size reaches 0.85 million characters.


include ate having the subject my cat, the object the food and the time modifier Yesterday, and the food having the location modifier in (the bowl).

different sets of GRs are useful for different purposes.


This liaison, which is represented by a phoneme appearing at the end of some words, must be removed when the next word begins with a consonant since the liaison phoneme is never pronounced in that case.

if the next word begins with a vowel, the liaison phoneme may or may not be pronounced and thus becomes optional.


Table2: BETA evaluation for the Ro-EN lexicon of nouns; both COGN and DIST filters used The analysis of the wrong translation pairs revealed that most of them were hapax pairs (pairs appearing only once) and they were selected because the DIST measure enabled them, so we considered that this filter is not discriminative enough for hapaxes.

for the non-hapax pairs the DIST condition was successful in more than 85% of the cases.


The treatment of verb + preposition cooccurrences is different from the treatment of N+P pairs since verb and preposition are seldom adjacent to each other in a German sentence.

they can be far apart from each other, the only restriction being that they cooccur within the same clause.


Of course, our definition of the term paradigmatic association as given in the introduction implies this.

the simulation system never obtained any information on part of speech, and so it is nevertheless surprising that -besides computing term similarities -it implicitly seems to be able to cluster parts of speech.


Paradigmatic associations like blue red, cold hot, and tobacco cigarette are intuitively plausible.

a quantitative evaluation would be preferable, of course, and for this reason we did a comparison with the results of the human subjects in the TOEFL test.


Again, we need to emphasize that parameters other than the basic methodology could have influenced the result, so we need to be cautious with an interpretation.

to us it seems that the view that some of the co-occurrences in corpora should be considered as noise is wrong, or else if there is some noise it obviously cancels out over large corpora.


In the ranked lists produced by the system we find a mixture of both types of associations.

for a given association there is no indication whether it is of syntagmatic or paradigmatic type.


In essence, this means that in the process of learning or generating associations the human mind seems to conduct operations that are equivalent to co-occurrence counting, to performing significance tests, or to computing vector similarities (see also Landauer & Dumais, 1997).

further work is required to find out to what extent other language-related tasks can also be explained statistically.


1993;Grishman, 1994;Meyers, Yanharber & Grishman 1996;Watanabe, Kurohashi & Aramaki 2000).

the mismatching between complex structures across languages and the poor parsing accuracy of the parser will hinder structure alignment algorithms from working out high accuracy results.


English NE identification has achieved a great success.

for Chinese, NE identification is very different.


The reason is that adopting heuristic information reduces the noise influence.

we noticed that the recall of PER and LOC decreased a bit.


In sentence translation, the alignment links frequently cross and it is not unusual for two words in different parts of sentences to correspond.

the processes that lead to link intersection in diachronic phonology, such as metathesis, are quite sporadic.


The quality of correspondences produced by CORDI is difficult to validate, quantify, and compare with the results of alternative approaches.

it is possible to evaluate the correspondences indirectly by using them to identify cognates.


As expected, Method A is outperformed by methods that employ an explicit noise model.

in spite of its extra complexity, Method C is not consistently better than Method B, perhaps because of its inability to detect important vowel-consonant correspondences, such as the ones between French nasal vowels and Latin /n/.


These methods could archive high accuracy because of the assumption of the sentence alignments for parallel corpora, but they have the problem with narrow applicable domains because there are not too many parallel corpora with sentence alignments at present.

because our method does not require sentence alignments, it can be applied for wider applicable domains.


These results show that there was only a negligible (and, according to the χ 2 test, statistically insignificant) difference between the results in the cases when the tagger was both trained and tested on "old" corpus and both trained and tested on the "corrected" corpus.

the difference in the error rate when the tagger was once trained on the "old" and once on the "corrected" version, and then in both cases tested on the "corrected" version 10 , brought up a relative error improvement of 9,97%.


In sentence (9), the airlines are the literal, and the persons the real referents.

relating these two entities directly by an employment relation is problematic, since it is impossible to connect the locality information (from Boston to New York) and the first class restriction to either of them.


Icons are now used in nearly all possible areas of human computer interaction, even office software or operating systems.

there are contexts where richer information has to be managed, for instance: Alternative & Augmentative Communication systems designed for the needs of speech or language im-paired people, to help them communicate (with icon languages like Minspeak, Bliss, Commun-I-Mage); Second Language Learning systems where learners have a desire to communicate by themselves, but do not master the structures of the target language yet; Cross-Language Information Retrieval systems, with a visual symbolic input.


In these contexts, the use of icons has many advantages: it makes no assumption about the language competences of the users, allowing impaired users, or users from a different linguistic background (which may not include a good command of one of the major languages involved in research on natural language processing), to access the systems; it may trigger a communication-motivated, implicit learning process, which helps the users to gradually improve their level of literacy in the target language.

icons suffer from a lack of expressive power to convey ideas, namely, the expression of abstract relations between concepts still requires the use of linguistic communication.


An approach to tackle this limitation is to try to "analyse" sequences of icons like natural language sentences are parsed, for example.

icons do not give grammatical information as clues to automatic parsers.


We thus should have to use a parser based on computing the dependencies, such as some which have been written to cope with variable-word-order languages (Covington, 1990).

since no morphological clue is available either to tell that an icon is, e.g., accusative or dative, we have to rely on semantic knowledge to guide role assignment.


(1998) did make use of information from the whole document.

their system is a hybrid of hand-coded rules and machine learning methods.


In the sentence Even News Broadcasting Corp., noted for its accurate reporting, made the erroneous announcement., a NER may mistake Even News Broadcasting Corp. as an organization name.

it is unlikely that other occurrences of News Broadcasting Corp. in the same document also co-occur with Even.


Borthwick (1999) successfully made use of other handcoded systems as input for his MENE system, and achieved excellent results.

such an approach requires a number of hand-coded systems, which may not be available in languages other than English.


The success stories of these systems indicated that carefully crafted answer patterns were effective in candidate answer extraction.

just applying answer patterns blindly might lead to disastrous results, as shown by Hermjakob (2002), since correct and incorrect answers were equally likely to match these patterns.


(2001) also used WordNet to assist in answering definition questions.

they took the hypernyms of the term to be defined as the default answers while we used its glosses.


The TREC-10 questions are typical instances of queries for which users tend to believe answers can be found from the web.

the candidate answers extracted from the web have to find support in the TREC-10 corpus in order to be judged as correct otherwise they will be marked as unsupported.


The best results were achieved when evidence from both resources was used.

it also demonstrates the difficulty of improving performance on very hard questions (d>=0.75).


We showed that using either approach alone improved MRR score by 19% and PCT5 score by 5% over the baseline.

the best performance was achieved when both methods were used together.


Since large tagged corpora in Bulgarian are not widely available, the development of a corpus-based probabilistic tagger was an unrealistic goal for us.

as some studies suggest (Voutilainen, 1995), the precision of rule-based taggers may exceed that of the probabilistic ones.


Third Construction verbs like versprechen allow a great deal of variation in the size of the left-peripherally shared topology area (LS=1:6), thereby licensing optional promotion of es.

since es is a personal pronoun, it only takes M2 as its landing site (see Table 3).


Knowledge of verb selectional preferences and verb subcategorization frames (SFs) can be extracted from corpora for use in various NLP tasks.

knowledge of SFs is often not fine-grained enough to distinguish various verbs and the kinds of arguments that they can select.


Each of the verbs above occurs with both the intransitive and transitive SFs.

the verbs differ in their underlying argument structure.


Both S1 and S2 receive credit for matching question words "Lee Harvey Oswald" and "kill" (underlined), as well as for finding an answer (bold) of the proper qtarget type (PROPER-PERSON).

is the answer "Jack Ruby" or "President John F. Kennedy"?


These methods can be viewed as an established basis for exposing hidden associations between documents and terms.

their objective is to generate a compact representation of the original information space, and it is likely in consequence that the resulting orthogonal vectors are dense with many non-zero elements (Dhillon and Modha, 1999).


For high-frequency terms, In the original definition, the value of δ was uniquely determined, for example as δ = m(1) M with m(1) being the number of terms that appear exactly once in the text.

we experimentally vary the value of δ in our study, because it is an essential factor for controlling the size and quality of the generated clusters.


6and 7: Without discounting, the value of δI(S T , S D ) in the above equation is always negative or zero.

with discounting, the value becomes positive for uniformly dense clusters, because the frequencies of individual cells are always smaller than their agglomeration and so the discounting effect is stronger for the former.


For comparison purposes, we have used only the conventional documents-and-terms feature space in our experiments.

the proposed micro-clustering framework can be applied more flexibly to other cases as well.


This process is currently implemented in the context of a BN.

any representation that supports the generation of a connected argument involving a given set of propositions would be appropriate.


Here, ¡ X is possible only when the statistics concerned use summation for aggregation.

if X turns into a uniquely instantiated variable, delete the aspect related to X from the perspective.


Conventionally unknown words were extracted by statistical methods for statistical methods are simple and efficient.

the statistical methods without using linguistic knowledge suffer the drawbacks of low precision and low recall.


Conventional statistical extraction methods are simple and efficient.

if without supporting linguistic evidences the precision of extraction is still not satisfactory, since a high frequency character string might be a phrase or a partial phrase instead of a word.


The above results could be justified by the structural difference of Japanese and English, where English takes the prefix structure that places emphasis at the beginning of a sentence, hence prefers left-to-right decoding.

japanese takes postfix structure, setting attention around the end of a sentence, therefore favors right-to-left decoding.


Therefore, the narrowing the search space by the beam search crite-ria (pruning) would not affect the overall quality.

if right-to-left decoding method were applied to such a language above, the difference of good hypotheses and bad hypotheses is small, hence the drop of hypotheses would affect the quality of translation.


most machine learning methods overfit the training data when many features are given.


Non-linear decision surfaces can be realized by replacing the inner product of (4) with a kernel function K(x • x i ) : In this paper, we use polynomial kernel functions that have been very effective when applied to other tasks, such as natural language processing (Joachims, 1998;Kudo and Matsumoto, 2001; Kudo and Matsumoto, 2000): 2.2 Sentence Ranking by using Support Vector Machines Important sentence extraction can be regarded as a two-class problem: important or unimportant.

the proportion of important sentences in training data will differ from that in the test data.


First, we show that an NE recognizer based on Support Vector Machines (SVMs) gives better scores than conventional systems.

off-the-shelf SVM classifiers are too inefficient for this task.


SVMs have given high performance in various classification tasks (Joachims, 1998;Kudo and Matsumoto, 2001).

it turned out that off-the-shelf SVM classifiers are too inefficient for NE recognition.


For instance, when we removed 5,066 features that appeared four times or less in the training data, the modified classifier for ORGANIZATION-END misclassified 103 training examples, whereas the original classifier misclassified only 19 examples.

xQK-FS removed 12,141 features without an increase in misclassifications for the training data.


GENERAL's F-measure was slightly improved from 87.04% to 87.10%.

when we trained the cubic kernel classifiers by using only features that appeared three times or more (without considering weights), TinySVM's classification time was reduced by only 14% and the F-measure was slightly degraded to 86.85%.


(1) Multilingual distinction of senses The developed method is based on the premise that the senses of a polysemous word in a language are lexicalized differently in another language.

the premise is not always true; that is, the ambiguity of a word may be preserved by its translations.


A repair is further classied into adjacent and long-distance.

implicit long-distance repair is generally not acceptable in Japanese.


They rely on partial matching between the word fragment and the following word to detect hesitations.

we cannot reasonably assume that current s p e e c h recognizers can recognize a word fragment as a correct word.


Some methods such as (Bear et al., 1992) process self-corrections sentence by sentence.

such approaches are not suitable for use with incremental parsing (Nakano and Shimazu, 1998).


This interpretation has an advantage in that we can handle various self-corrections uniformly by supplementing omitted verbs.


The third assumption (and the condition of step 3 concerning coherency in gure 7) means that even a human cannot make a unique interpretation if the word correspondence between t 1 and t 2 is ambiguous.


In the example in gure 10, our method creates a correspondence between \mae ni " a n d \ atari ni " and copies \kamera no" t o \ atari ni."

it should be attached to \mae no" instead of \atari ni."


We can nd most self-correction candidates with these types of information.

we need more information to evaluate the validity of each candidate.


order of C, A, B, because hypothesis C contains the most words.

the policy that causes this problem is needed to prevent the parser from removing too much information.


In order to know which particular meaning of the word we want to oppose, we have to assess by what context meaning has to be constrained.

context is not always sufficient to give a symmetry axis for antonymy.


His analysis has to deal with the notion of context: he proposes to associate to a word a core semantic description (the fact that a "door" is an "aperture") and to add some additional features, which can be activated in context ("walk-through" is the telic role of a "door").

pustejovsky does not take into account important notions such as lexical chains and text coherence.


Although clustering proves to be an alternative to lists, it seeks a global, possibly nested partition in which clusters represent sets of indistinguishable objects regarding the cluster criterion.

to this, we present cohesion trees (CT) as a data structure, in which single objects are hierarchically ordered on the basis of lexical cohesion.


Their concept is similar to that of our model, which can be regarded as a model that translates keywords into text, while their model can be regarded as one that translates query words into documents.

the purpose of their model is different: their goal is to retrieve text that already exists while ours is to generate new text.


We cannot distinguish the role using only POS since it varies -attributes are not always represented by adjectives nor actions by verbs (compare intense price competition with price competition is intensifying this month.).

the words in the second class have indirect relations, e.g., association, with the target word.


For almost all sets, context word vector 2 provides higher precision than context word vector 1.

the effect of consideration of syntactic dependency is minimal in this experiment.


This problem cannot be resolved by this method alone.

there is still room for improvement by combining other information, e.g., the similarity between components.


The inference of structure in XML information is a relatively new area of research.

there are several closely related topics that have been studied for a longer period.


However, their inference algorithms are based upon direct gen-eralisation and factoring of regular expressions, with information theoretic principles used to choose a final result from a pool of candidates.

we propose a hybrid method which employs various principles throughout the inference process, with the aim of producing a more general method.


Thus the newer method is preferable if only one choice is allowed.

the sk-ALL heuristic was better in terms of worstcase performance, though the deviation is still rather high.


Information extraction (IE) is an important application for natural language processing, and recent research has made great strides toward making IE systems easily portable across domains.

iE systems depend on parsing tools and specialized dictionaries that are language specific, so they are not easily portable across languages.


And the only elements in the distribution are words, which appeared in texts.

lSA and PlSA expressed the latent semantic structures, called a topic of the context.


From this, we could not declare whether the value of k and translation accuracy have relationship of each other or not in the data-driven models described in this paper.

we could also find that the degree of accuracy was raised in accordance with the value of k in PLSA.


A distributional soundness is expected to have better performance as the size of examples is growing.

we should resolve several problems raised during the experiment.


That is, the classification of TCFP requires a simple calculation in proportion to the number of unique terms in the test document.

in k-NN, a search in the whole training space must be done for each test document.


In more detail, the kernel of our novel disambiguation method for UBGs consists of the application of a context-free approximation for a given UBG  and the exploitation of the standard probability model for CFGs.

to earlier approaches to disambiguation for UBGs, our approach has several advantages.


(2000) achieved a precision, which is only slightly lower than ours.

their results were yielded using a corpus, which is about 80 times as big as ours.


Figure 9 shows that the rst proposed system based on TM3LM-score achieved the greatest improvement of a little under 6%, in the performance for Rank A.

the existing selection system simply using the LM-score (language model of the translation target) could not improve a n d e v en degraded performance for Rank A.


Figure 10 shows that the second proposed system with the pruning procedure based on both TM3LM-score and TM-score (marked RT12-PRN in the graph) achieved the greatest improvement of about 5%, for Rank A+B (equal to or higher than B).

figure 10 shows that the existing selection system   figure 10: Dierence in performance between each selection system and J-E TDMT.


Statistical approaches (Aone and Bennett, 1995;Ge et al., 1998;Kim and Ehara, 1995;Soon et al., 2001) use statistical models produced based on corpora annotated with anaphoric relations.

only a few attempts have been made in corpus-based anaphora resolution for Japanese zero pronouns.


Ideally, in the case where a verb in question is polysemous, word sense disambiguation is needed to select the appropriate case frame, because different verb senses often correspond to different case frames.

we currently merge multiple case frames for a verb into a single frame so as to avoid the polysemous problem.


In the case where the amount of unannotated corpora was varied in producing a semantic model, the accuracy marginally improved as the corpus size increases.

note that we do not need human supervision to produce a semantic model.


In their model, the search scope for possible antecedents was limited to the sentence containing zero pronouns.

our method can resolve zero pronouns in both intra/inter-sentential anaphora types.